Munge data

# Load libraries
library(readr)
library(dplyr)
library(tidyr)
library(readxl)
library(stringr)
library(ggplot2)
library(gridExtra)
library(scales)
library(Cairo)
library(grid)
library(vcd)
library(countrycode)

# Load data and add labels
# TODO: Use non-unicode-mangled data
load(file.path(PROJHOME, "data_raw", "responses_orgs.RData"))
load(file.path(PROJHOME, "data_raw", "responses_countries.RData"))
responses.orgs.labs <- read_csv(file.path(PROJHOME, "data_raw", "response_orgs_labels.csv"))
responses.countries.labs <- read_csv(file.path(PROJHOME, "data_raw", "response_countries_labels.csv"))

Hmisc::label(responses.orgs, self=FALSE) <- responses.orgs.labs$varlabel
Hmisc::label(responses.countries, self=FALSE) <- responses.countries.labs$varlabel

# Add a nicer short ID to each response to reference in the article
set.seed(1234)
nicer.ids <- data_frame(survey.id = responses.orgs$survey.id) %>%
  mutate(clean.id = 1000 + sample(1:n()))
write_csv(nicer.ids, path=file.path(PROJHOME, "data", "id_lookup_WILL_BE_OVERWRITTEN.csv"))

The data is split into two sets: responses.orgs has a row for each surveyed organization and responses.countries has a row for each country organizations responded for (1-4 countries per organization). For ease of analysis, this combines them into one larger dataframe (so organization-level data is repeated). It also removes columns that were added manually, where an RA coded whether a country was mentioned in different questions (with a colum for each country!).

responses.all <- responses.orgs %>% 
  left_join(responses.countries, by="survey.id") %>%
  select(-contains("_c", ignore.case=FALSE)) %>%  # Get rid of all the dummy vars
  left_join(nicer.ids, by="survey.id") %>%
  select(survey.id, clean.id, everything())

Convert some responses into numeric indexes:

importance <- data_frame(Q3.19 = levels(responses.countries$Q3.19),
                         importance = c(2, 1, 0, NA))
importance.levels <- data_frame(start=c(0, 1, 2),
                                end=c(1, 2, 3),
                                level=c("Not important", "Somewhat important", 
                                        "Most important"),
                                level.ordered=factor(level, levels=level, ordered=TRUE))

positivity <- data_frame(Q3.25 = levels(responses.countries$Q3.25),
                         positivity = c(-1, 1, 0, NA))
improvement <- data_frame(Q3.26 = levels(responses.countries$Q3.26),
                          improvement = c(1, 0, -1, NA))

# Cho data
# TODO: Someday get this directly from the internet, like Freedom House data
# http://www.economics-human-trafficking.org/data-and-reports.html
tip.change <- read_csv(file.path(PROJHOME, "data", "policy_index.csv")) %>%
  group_by(countryname) %>%
  summarise(avg_tier = mean(tier, na.rm=TRUE),
            change_tip = -(last(na.omit(tier), default=NA) - 
                             first(na.omit(tier), default=NA)),
            change_policy = last(na.omit(p), default=NA) - 
              first(na.omit(p), default=NA)) %>%
  mutate(countryname = countrycode(countryname, "country.name", "country.name"))

# Democracy (Freedom House)
fh.url <- "https://freedomhouse.org/sites/default/files/Individual%20Country%20Ratings%20and%20Status%2C%201973-2015%20%28FINAL%29.xls"
fh.tmp <- paste0(tempdir(), basename(fh.url))
download.file(fh.url, fh.tmp)

fh.raw <- read_excel(fh.tmp, skip=6)
## DEFINEDNAME: 20 00 00 01 1a 00 00 00 01 00 00 00 00 00 00 07 29 17 00 3b 00 00 00 00 ff ff 00 00 00 00 3b 00 00 00 00 06 00 00 00 ff 00 10 
## DEFINEDNAME: 20 00 00 01 1a 00 00 00 01 00 00 00 00 00 00 07 29 17 00 3b 00 00 00 00 ff ff 00 00 00 00 3b 00 00 00 00 06 00 00 00 ff 00 10 
## DEFINEDNAME: 20 00 00 01 1a 00 00 00 01 00 00 00 00 00 00 07 29 17 00 3b 00 00 00 00 ff ff 00 00 00 00 3b 00 00 00 00 06 00 00 00 ff 00 10 
## DEFINEDNAME: 20 00 00 01 1a 00 00 00 01 00 00 00 00 00 00 07 29 17 00 3b 00 00 00 00 ff ff 00 00 00 00 3b 00 00 00 00 06 00 00 00 ff 00 10
# Calculate the number of years covered in the data (each year has three columns)
num.years <- (ncol(fh.raw) - 1)/3

# Create combinations of all the variables and years
var.years <- expand.grid(var = c('PR', 'CL', 'Status'), 
                         year = 1972:(1972 + num.years - 1))

colnames(fh.raw) <- c('country', paste(var.years$var, var.years$year, sep="_"))

# Split columns and convert to long
fh <- fh.raw %>%
  gather(var.year, value, -country) %>%
  separate(var.year, into=c("indicator", "year"), sep="_") %>%
  filter(!is.na(country)) %>%
  spread(indicator, value) %>%
  mutate(year = as.numeric(year),
         CL = suppressWarnings(as.integer(CL)),
         PR = suppressWarnings(as.integer(PR)),
         Status = factor(Status, levels=c("NF", "PF", "F"), 
                         labels=c("Not free", "Partially free", "Free"),
                         ordered=TRUE),
         total.freedom = CL + PR,
         country.clean = countrycode(country, "country.name", "country.name")) %>%
  filter(!is.na(CL) & !is.na(PR)) %>%
  # All the cases we're interested in are after 2000, so we can remove these
  # problematic double countries
  filter(!(country %in% c("Germany, E.", "Germany, W.", "USSR", "Vietnam, N.", 
                          "Vietnam, S.", "Yemen, N.", "Yemen, S."))) %>%
  # Again, because we only care about post-2000 Serbia, merge with Yugoslavia
  mutate(country.clean = ifelse(country.clean == "Yugoslavia", 
                                "Serbia", country.clean)) %>%
  select(-country, country=country.clean)

fh.summary <- fh %>%
  filter(year >= 2000) %>%
  group_by(country) %>%
  summarize(total.freedom = mean(total.freedom, na.rm=TRUE))

# Funding
funding.raw <- read_csv(file.path(PROJHOME, "data_raw", "funding_clean.csv")) %>%
  mutate(cowcode = ifelse(country == "Serbia", 555, cowcode),
         countryname = countrycode(cowcode, "cown", "country.name"),
         countryname = ifelse(cowcode == 555, "Serbia", countryname)) %>%
  filter(!is.na(countryname)) 

funding.all <- funding.raw %>%
  group_by(countryname) %>%
  summarise(total.funding = sum(amount, na.rm=TRUE),
            avg.funding = mean(amount, na.rm=TRUE)) 

funding.ngos <- funding.raw %>%
  filter(recipient_type %in% c("NGO", "NPO")) %>%
  group_by(countryname) %>%
  summarise(total.funding.ngos = sum(amount, na.rm=TRUE),
            avg.funding.ngos = mean(amount, na.rm=TRUE)) 

responses.full <- responses.all %>%
  filter(work.only.us != "Yes") %>%
  mutate(work.country.clean = countrycode(work.country, 
                                          "country.name", "country.name"),
         work.country.clean = ifelse(is.na(work.country), 
                                     "Global", work.country.clean),
         work.country = work.country.clean) %>%
  left_join(tip.change, by=c("work.country" = "countryname")) %>%
  left_join(funding.all, by=c("work.country" = "countryname")) %>%
  left_join(funding.ngos, by=c("work.country" = "countryname")) %>%
  left_join(fh.summary, by=c("work.country" = "country")) %>%
  left_join(positivity, by = "Q3.25") %>%
  left_join(importance, by = "Q3.19") %>%
  left_join(improvement, by = "Q3.26") %>%
  mutate(received.funding = ifelse(Q3.18_3 != 1 | is.na(Q3.18_3), FALSE, TRUE),
         us.involvement = ifelse(Q3.18_5 != 1 | is.na(Q3.18_5), TRUE, FALSE),
         Q3.19 = factor(Q3.19, levels=c("Most important actor", 
                                        "Somewhat important actor", 
                                        "Not an important actor", 
                                        "Don't know"),
                        ordered=TRUE),
         Q3.25_collapsed = ifelse(Q3.25 == "Negative", NA, Q3.25))

country.indexes <- responses.full %>%
  filter(!is.na(work.country)) %>%
  group_by(work.country) %>%
  # Needs mutate + mutate_each + select + unique because you can't mix 
  # summarise + summarise_each. See http://stackoverflow.com/a/31815540/120898
  mutate(num.responses = n()) %>%
  mutate_each(funs(score = mean(., na.rm=TRUE), stdev = sd(., na.rm=TRUE), 
                   n = sum(!is.na(.))),
              c(positivity, importance, improvement)) %>%
  select(work.country, num.responses, matches("positivity_|importance_|improvement_")) %>%
  unique %>%
  ungroup() %>%
  arrange(desc(num.responses))


# Useful functions
theme_clean <- function(base_size=9, base_family="Source Sans Pro Light") {
  ret <- theme_bw(base_size, base_family) + 
    theme(panel.background = element_rect(fill="#ffffff", colour=NA),
          axis.title.x=element_text(vjust=-0.2), axis.title.y=element_text(vjust=1.5),
          title=element_text(vjust=1.2, family="Source Sans Pro Semibold"),
          panel.border = element_blank(), axis.line=element_blank(),
          panel.grid.minor=element_blank(),
          panel.grid.major.y = element_blank(),
          panel.grid.major.x = element_line(size=0.25, colour="grey90"),
          axis.ticks=element_blank(),
          legend.position="bottom", 
          axis.title=element_text(size=rel(0.8), family="Source Sans Pro Semibold"),
          strip.text=element_text(size=rel(0.9), family="Source Sans Pro Semibold"),
          strip.background=element_rect(fill="#ffffff", colour=NA),
          panel.margin=unit(1, "lines"), legend.key.size=unit(.7, "line"),
          legend.key=element_blank())
  
  ret
}

# Return a data frame of counts and proportions for multiple responses
separate.answers.summary <- function(df, cols, labels, total=FALSE) {
  cols.to.select <- which(colnames(df) %in% cols)
  
  denominator <- df %>%
    select(cols.to.select) %>%
    mutate(num.answered = rowSums(., na.rm=TRUE)) %>%
    filter(num.answered > 0) %>%
    nrow()
  
  df <- df %>%
    select(survey.id, cols.to.select) %>%
    gather(question, value, -survey.id) %>%
    mutate(question = factor(question, labels=labels, ordered=TRUE)) %>%
    group_by(question) %>%
    summarize(response = sum(value, na.rm=TRUE), 
              pct = round(response / denominator * 100, 2),
              plot.pct = response / denominator)
  
  colnames(df) <- c("Answer", "Responses", "%", "plot.pct")
  
  if (total) {
    df <- df %>% select(1:3)
    df <- rbind(as.matrix(df), c("Total responses", denominator, "—"))
  }
  
  return(list(df=df, denominator=denominator))
}

# Create a character vector of significance stars
add.stars <- function(x) {
  as.character(symnum(x, corr = FALSE,
                      cutpoints = c(0,  .001,.01,.05, .1, 1),
                      symbols = c("***","**","*","."," ")))
}

NGO opinions of US activity

# Select just the columns that have cowcodes embedded in them
active.embassies.raw <- responses.countries %>%
  select(contains("_c", ignore.case=FALSE)) %>%
  mutate_each(funs(as.numeric(levels(.))[.]))  # Convert values to numeric

# Select only the rows where they responded (i.e. not all columns are NA)
num.responses <- active.embassies.raw %>%
  rowwise() %>% do(all.missing = all(!is.na(.))) %>%
  ungroup() %>% mutate(all.missing = unlist(all.missing)) %>%
  summarise(total = sum(all.missing))

# Tidy cowcode columns and summarize most commonly mentioned countries
active.embassies <- active.embassies.raw %>%
  gather(country.raw, num) %>%
  group_by(country.raw) %>% summarise(num = sum(num, na.rm=TRUE)) %>%
  mutate(country.raw = str_replace(country.raw, "Q.*c", ""),
         country = countrycode(country.raw, "cown", "country.name"),
         country = ifelse(country.raw == "2070", "European Union", country)) %>%
  ungroup() %>% mutate(prop = num / num.responses$total,
                       prop.nice = sprintf("%.1f%%", prop * 100))

Which embassies or foreign governments NGOs were reported as active partners in the fight against human trafficking?

active.embassies.top <- active.embassies %>%
  arrange(num) %>% select(-country.raw) %>%
  filter(num > 10) %>%
  mutate(country = factor(country, levels=country, ordered=TRUE)) %>%
  arrange(desc(num))

active.embassies %>% arrange(desc(num)) %>% 
  select(-country.raw) %>% filter(num > 10)
## Source: local data frame [12 x 4]
## 
##      num        country       prop prop.nice
##    (dbl)          (chr)      (dbl)     (chr)
## 1    260  United States 0.76923077     76.9%
## 2     43 United Kingdom 0.12721893     12.7%
## 3     35    Netherlands 0.10355030     10.4%
## 4     30         France 0.08875740      8.9%
## 5     26         Norway 0.07692308      7.7%
## 6     26 European Union 0.07692308      7.7%
## 7     24    Switzerland 0.07100592      7.1%
## 8     21         Sweden 0.06213018      6.2%
## 9     19      Australia 0.05621302      5.6%
## 10    17        Germany 0.05029586      5.0%
## 11    17          Italy 0.05029586      5.0%
## 12    12         Canada 0.03550296      3.6%
nrow(active.embassies)  # Number of countries mentioned
## [1] 64
num.responses$total  # Total responses
## [1] 338
# Most active embassies
# Save Q3.7 to a CSV for hand coding
most.active <- responses.countries %>%
  select(Q3.7) %>%
  filter(!is.na(Q3.7))
write_csv(most.active, path=file.path(PROJHOME, "data", 
                                      "most_active_WILL_BE_OVERWRITTEN.csv"))

# Read in hand-coded CSV
if (file.exists(file.path(PROJHOME, "data", "most_active.csv"))) {
  most.active <- read_csv(file.path(PROJHOME, "data", "most_active.csv"))
} else {
  stop("data/most_active.csv is missing")
}

# Split comma-separated countries, unnest them into multiple rows, and 
# summarize most active countries
most.active.clean <- most.active %>%
  transform(clean = strsplit(clean, ",")) %>%
  unnest(clean) %>%
  mutate(clean = str_trim(clean)) %>%
  group_by(clean) %>%
  summarise(total = n()) %>%
  mutate(prop = total / nrow(most.active),
         prop.nice = sprintf("%.1f%%", prop * 100))

Which countries or embassies have been the most active?

most.active.clean %>% arrange(desc(total))
## Source: local data frame [40 x 4]
## 
##             clean total       prop prop.nice
##             (chr) (int)      (dbl)     (chr)
## 1   United States   188 0.70149254     70.1%
## 2            None    16 0.05970149      6.0%
## 3  European Union    14 0.05223881      5.2%
## 4             All    12 0.04477612      4.5%
## 5     Switzerland     8 0.02985075      3.0%
## 6       Australia     7 0.02611940      2.6%
## 7           Italy     7 0.02611940      2.6%
## 8  United Kingdom     7 0.02611940      2.6%
## 9     Netherlands     6 0.02238806      2.2%
## 10         Norway     6 0.02238806      2.2%
## ..            ...   ...        ...       ...
nrow(most.active.clean) - 1  # Subtract one because of "None"s
## [1] 39

Over the last 10–15 years, has the United States or its embassy been active in the fight against human trafficking in X?

responses.countries$Q3.8 %>% table %>% print %>% prop.table
## .
##         No        Yes Don't know 
##         39        344        150
## .
##         No        Yes Don't know 
## 0.07317073 0.64540338 0.28142589

Side-by-side graph of active countries + most active countries

plot.data <- active.embassies.top %>%
  bind_rows(data_frame(num=0, country=c("All", "None"), 
                       prop=0, prop.nice="")) %>%
  arrange(num) %>%
  mutate(country = factor(country, levels=country, ordered=TRUE))

plot.data.active <- most.active.clean %>%
  filter(clean %in% plot.data$country) %>%
  mutate(country = factor(clean, levels=levels(plot.data$country), ordered=TRUE))

fig.active <- ggplot(plot.data, aes(x=country, y=num)) + 
  geom_bar(stat="identity") + 
  geom_text(aes(label = prop.nice), size=3.5, hjust=1.3, 
            family="Source Sans Pro Light") + 
  labs(x=NULL, y="Number of times country was mentioned as a partner in anti-TIP work") + 
  scale_y_continuous(breaks=seq(0, max(active.embassies$num), by=25), 
                     trans="reverse", expand = c(.1, .1)) + 
  coord_flip() + 
  theme_clean() + 
  theme(axis.text.y = element_blank(), 
        axis.line.y = element_blank(),
        plot.margin = unit(c(1,0.5,1,1), "lines"))

fig.most.active <- ggplot(plot.data.active, aes(x=country, y=total)) + 
  geom_bar(stat="identity") + 
  geom_text(aes(label = prop.nice), size=3.5, hjust=-0.3, 
            family="Source Sans Pro Light") + 
  labs(x=NULL, y="Number of times country was mentioned as the most active partner in anti-TIP work") + 
  scale_y_continuous(expand = c(.15, .15)) + 
  coord_flip() + 
  theme_clean() + 
  theme(axis.text.y = element_text(hjust=0.5), 
        axis.line.y = element_blank(),
        plot.margin = unit(c(1,1,1,0), "lines"))
  
fig.embassies <- arrangeGrob(fig.active, fig.most.active, nrow=1)
grid.draw(fig.embassies)

ggsave(fig.embassies, filename=file.path(PROJHOME, "figures", "fig_embassies.pdf"),
       width=5, height=2, units="in", device=cairo_pdf, scale=2.5)
ggsave(fig.embassies, filename=file.path(PROJHOME, "figures", "fig_embassies.png"),
       width=5, height=2, units="in", scale=2.5)

Actual US activities

cols <- c("Q3.9_1", "Q3.9_2", "Q3.9_3", "Q3.9_4", "Q3.9_5",
          "Q3.9_6", "Q3.9_7", "Q3.9_8", "Q3.9_9", "Q3.9_10")
labels <- c("Asking for legislation", "Convening conferences or workshops",
            "Raising awareness", "Providing resources or funding",
            "Increasing government attention", "Training government officials",
            "Contributing to a government action plan", "Other", "Don't know",
            "The US has not been involved in trafficking issues")

activities <- separate.answers.summary(responses.countries, cols, labels)
activities$denominator  # Number of responses
## [1] 532
activities$df
## Source: local data frame [10 x 4]
## 
##                                                Answer Responses     %   plot.pct
##                                                (fctr)     (int) (dbl)      (dbl)
## 1                              Asking for legislation       165 31.02 0.31015038
## 2                  Convening conferences or workshops       208 39.10 0.39097744
## 3                                   Raising awareness       214 40.23 0.40225564
## 4                      Providing resources or funding       212 39.85 0.39849624
## 5                     Increasing government attention       217 40.79 0.40789474
## 6                       Training government officials       146 27.44 0.27443609
## 7            Contributing to a government action plan       114 21.43 0.21428571
## 8                                               Other        43  8.08 0.08082707
## 9                                          Don't know        26  4.89 0.04887218
## 10 The US has not been involved in trafficking issues       166 31.20 0.31203008
plot.data <- activities$df %>% 
  mutate(Answer=factor(Answer, levels=rev(labels), ordered=TRUE))

fig.activities <- ggplot(plot.data, aes(x=Answer, y=Responses)) +
  geom_bar(aes(y=plot.pct), stat="identity") + 
  labs(x=NULL, y=NULL) + 
  scale_y_continuous(labels = percent, 
                     breaks = seq(0, max(round(plot.data$plot.pct, 1)), by=0.1)) + 
  coord_flip() + theme_clean()
fig.activities

ggsave(fig.activities, filename=file.path(PROJHOME, "figures", "fig_activities.pdf"),
       width=6.5, height=5, units="in", device=cairo_pdf)
ggsave(fig.activities, filename=file.path(PROJHOME, "figures", "fig_activities.png"),
       width=6.5, height=5, units="in")

NGO opinions of US importance

General importance

plot.data <- responses.full %>%
  group_by(Q3.19) %>%
  summarize(num = n()) %>%
  na.omit() %>%
  mutate(prop = num / sum(num),
         prop.nice = sprintf("%.1f%%", prop * 100),
         Q3.19 = factor(Q3.19, levels=rev(levels(Q3.19)), ordered=TRUE))
plot.data
## Source: local data frame [4 x 4]
## 
##                      Q3.19   num      prop prop.nice
##                     (fctr) (int)     (dbl)     (chr)
## 1     Most important actor   139 0.2662835     26.6%
## 2 Somewhat important actor   182 0.3486590     34.9%
## 3   Not an important actor    68 0.1302682     13.0%
## 4               Don't know   133 0.2547893     25.5%
fig.us_importance <- ggplot(plot.data, aes(x=Q3.19, y=prop)) + 
  geom_bar(stat="identity") + 
  labs(x=NULL, y=NULL) + 
  scale_y_continuous(labels = percent, 
                     breaks = seq(0, max(round(plot.data$num, 1)), by=0.1)) + 
  coord_flip() + theme_clean()
fig.us_importance

ggsave(fig.us_importance, filename=file.path(PROJHOME, "figures", "fig_us_importance.pdf"),
       width=6.5, height=5, units="in", device=cairo_pdf)
ggsave(fig.us_importance, filename=file.path(PROJHOME, "figures", "fig_us_importance.png"),
       width=6.5, height=5, units="in")

Average importance by country

importance.plot <- country.indexes %>%
  filter(num.responses >= 10) %>%
  arrange(importance_score) %>%
  mutate(country_label = factor(work.country, levels=unique(work.country), 
                                labels=paste0(work.country, " (", num.responses, ")"),
                                ordered=TRUE)) 

fig.avg_importance <- ggplot(importance.plot, aes(x=country_label, y=importance_score)) + 
  geom_rect(data=importance.levels, aes(x=NULL, y=NULL, ymin=start, ymax=end, 
                                        xmin=0, xmax=Inf, fill=level.ordered), alpha=0.5) + 
  geom_pointrange(aes(ymax=importance_score + importance_stdev,
                      ymin=importance_score - importance_stdev)) + 
  labs(x="Country (number of responses)", 
       y="Importance of the US in anti-TIP efforts (mean)") + 
  scale_fill_manual(values=c("grey90", "grey60", "grey30"), name=NULL) + 
  coord_flip() + 
  theme_clean() + theme(legend.position="bottom")
fig.avg_importance

ggsave(fig.avg_importance, filename=file.path(PROJHOME, "figures", "fig_avg_importance.pdf"),
       width=6.5, height=5, units="in", device=cairo_pdf)
ggsave(fig.avg_importance, filename=file.path(PROJHOME, "figures", "fig_avg_importance.png"),
       width=6.5, height=5, units="in")

Are opinions of the US’s importance associated with…?

df.importance <- responses.full %>% 
  select(Q3.19, work.country, change_policy, avg_tier, change_tip, change_policy, 
         importance, received.funding, us.involvement, total.funding, 
         total.freedom, time.spent=Q2.1) %>% 
  filter(!is.na(Q3.19)) %>%
  mutate(importance_factor = factor(Q3.19, ordered=FALSE),
         log.total.funding = log1p(total.funding),
         time.spent = as.numeric(time.spent))

Average tier rating

Average tier doesn’t show much because it doesn’t show any changes in time—just how bad the country is in general?

importance.means <- df.importance %>%
  group_by(Q3.19) %>%
  summarize(avg_points = mean(avg_tier, na.rm=TRUE),
            var_points = var(avg_tier, na.rm=TRUE)) %>%
  print
## Source: local data frame [4 x 3]
## 
##                      Q3.19 avg_points var_points
##                     (fctr)      (dbl)      (dbl)
## 1     Most important actor   2.053344  0.1171988
## 2 Somewhat important actor   1.920723  0.2256463
## 3   Not an important actor   1.763046  0.3492590
## 4               Don't know   1.744558  0.2864291

Plot group means and distributions

fig.importance <- ggplot(df.importance, aes(x=Q3.19, y=avg_tier)) +
  geom_violin(fill="grey90") + 
  geom_point(alpha=0.05, show.legend=FALSE) +
  geom_point(data=importance.means, aes(x=Q3.19, y=avg_points), size=5, show.legend=FALSE) + 
  labs(x="Opinion of US importance", y="Average TIP tier rating") + 
  coord_flip() + theme_clean()
fig.importance

Those means appear slightly different from each other. Is that really the case? Check with ANOVA, which assumes homogenous variance across groups. Throw every possible test at it—if null is rejected (p < 0.05 or whatever) then variance is likely heterogenous: (helpful reference)

bartlett.test(avg_tier ~ importance_factor, data=df.importance)
## 
##  Bartlett test of homogeneity of variances
## 
## data:  avg_tier by importance_factor
## Bartlett's K-squared = 35.422, df = 3, p-value = 9.923e-08
car::leveneTest(avg_tier ~ importance_factor, data=df.importance)
## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group   3  14.266 6.052e-09 ***
##       517                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fligner.test(avg_tier ~ importance_factor, data=df.importance)  # Uses median
## 
##  Fligner-Killeen test of homogeneity of variances
## 
## data:  avg_tier by importance_factor
## Fligner-Killeen:med chi-squared = 28.052, df = 3, p-value = 3.542e-06
kruskal.test(avg_tier ~ importance_factor, data=df.importance)  # Nonparametric
## 
##  Kruskal-Wallis rank sum test
## 
## data:  avg_tier by importance_factor
## Kruskal-Wallis chi-squared = 23.591, df = 3, p-value = 3.041e-05

All of those p-values are tiny, so it’s clear that variance is not the same across groups. However, there’s a rule of thumb (super detailed example) that ANOVA is robust to heterogeneity of variance as long as the largest variance is less than four times the smallest variance.

Given that rule of thumb, the variance here isn’t that much of an issue

df.importance %>% group_by(importance_factor) %>%
  summarise(variance = var(avg_tier, na.rm=TRUE)) %>%
  do(data_frame(ratio = max(.$variance) / min(.$variance)))
## Source: local data frame [1 x 1]
## 
##      ratio
##      (dbl)
## 1 2.980057

It would be cool to use Bayesian ANOVA to account for non-homogenous variances (see John Kruschke’s evangelizing), since it handles violations of ANOVA assumptions nicely. However, in his example, the ratio of min/max variance is huge, so it does lead to big differences in results:

#
# read_csv("http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/Programs/NonhomogVarData.csv") %>%
#   group_by(Group) %>%
#   summarise(variance = var(Y)) %>%
#   do(data_frame(ratio = max(.$variance) / min(.$variance)))
#   # ratio = 64
#

With the variance issue handled, run the ANOVA:

importance.aov <- aov(avg_tier ~ importance_factor, data=df.importance)
summary(importance.aov)
##                    Df Sum Sq Mean Sq F value  Pr(>F)    
## importance_factor   3   7.79  2.5963   11.38 3.1e-07 ***
## Residuals         517 118.00  0.2282                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

There is some significant difference between groups. Look at pairwise comparisons between all the groups to (kind of) decompose that finding:

(importance.pairs <- TukeyHSD(importance.aov, "importance_factor"))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = avg_tier ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                       diff        lwr          upr     p adj
## Somewhat important actor-Most important actor   -0.1326211 -0.2714899  0.006247627 0.0673115
## Not an important actor-Most important actor     -0.2902984 -0.4725198 -0.108076873 0.0002725
## Don't know-Most important actor                 -0.3087859 -0.4581435 -0.159428195 0.0000009
## Not an important actor-Somewhat important actor -0.1576772 -0.3328160  0.017461509 0.0947138
## Don't know-Somewhat important actor             -0.1761647 -0.3167941 -0.035535362 0.0072161
## Don't know-Not an important actor               -0.0184875 -0.2020542  0.165079256 0.9938662

Plot the differences:

importance.pairs.plot <- data.frame(importance.pairs$importance_factor) %>%
  mutate(pair = row.names(.),
         pair = factor(pair, levels=pair, ordered=TRUE),
         stars = add.stars(p.adj))

fig.importance.pairs <- ggplot(importance.pairs.plot, 
                               aes(x=pair, y=diff, ymax=upr, ymin=lwr)) + 
  geom_hline(yintercept=0) + 
  geom_text(aes(label=stars), nudge_x=0.25) +
  geom_pointrange() + 
  theme_clean() + coord_flip()
fig.importance.pairs

Another way of checking group means in non-homogenous data is to use ordinal logistic regression. Here’s an ordered logit and corresponding predicted probabilities:

model.importance <- ordinal::clm(Q3.19 ~ avg_tier, data=df.importance, 
                                 link="logit", Hess=TRUE)
summary(model.importance)
## formula: Q3.19 ~ avg_tier
## data:    df.importance
## 
##  link  threshold nobs logLik  AIC     niter max.grad cond.H 
##  logit flexible  521  -679.73 1367.46 6(0)  1.69e-12 2.1e+02
## 
## Coefficients:
##          Estimate Std. Error z value Pr(>|z|)    
## avg_tier  -0.8863     0.1618  -5.479 4.27e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Threshold coefficients:
##                                                 Estimate Std. Error z value
## Most important actor|Somewhat important actor    -2.7248     0.3305  -8.244
## Somewhat important actor|Not an important actor  -1.1795     0.3119  -3.782
## Not an important actor|Don't know                -0.5470     0.3092  -1.769
## (1 observation deleted due to missingness)
# Predicted probabilities
newdata <- data_frame(avg_tier = seq(0, 3, 0.1))
pred.importance <- predict(model.importance, newdata, interval=TRUE)

# Create plot data
pred.plot.lower <- cbind(newdata, pred.importance$lwr) %>%
  gather(importance, lwr, -c(1:ncol(newdata)))
pred.plot.upper <- cbind(newdata, pred.importance$upr) %>%
  gather(importance, upr, -c(1:ncol(newdata)))

pred.plot.data <- cbind(newdata, pred.importance$fit) %>%
  gather(importance, importance_prob, -c(1:ncol(newdata))) %>%
  left_join(pred.plot.lower, by=c("avg_tier", "importance")) %>%
  left_join(pred.plot.upper, by=c("avg_tier", "importance"))

importance.colors <- c("grey20", "grey40", "grey60", "grey80")
ggplot(pred.plot.data, aes(x=avg_tier, y=importance_prob)) +  
  geom_ribbon(aes(ymax=upr, ymin=lwr, fill=importance), 
              alpha=0.2) + 
  geom_line(aes(colour=importance), size=2) + 
  scale_y_continuous(labels=percent) + 
  labs(x="Average tier rating in country", 
       y="Predicted probability of assigning importance") + 
  # scale_fill_manual(values=importance.colors, name=NULL) + 
  # scale_colour_manual(values=importance.colors, name=NULL) +
  theme_clean()

Change in TIP scores

Opinions of importance are not related to changes in TIP score. The average change in TIP rating is the same for each possible answer of importance.

ggplot(df.importance, aes(x=Q3.19, y=change_tip)) + 
  geom_violin(fill="grey90") + 
  geom_point(stat="summary", fun.y="mean", size=5) + 
  labs(x="Opinion of US", y="Change in TIP tier rating") + 
  coord_flip() + theme_clean()

Variance is equal in all groups:

kruskal.test(change_tip ~ importance_factor, data=df.importance)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  change_tip by importance_factor
## Kruskal-Wallis chi-squared = 0.098657, df = 3, p-value = 0.992

ANOVA shows no differences:

change.anova <- aov(change_tip ~ importance_factor, data=df.importance) 
summary(change.anova)
##                    Df Sum Sq Mean Sq F value Pr(>F)
## importance_factor   3   0.16 0.05178   0.183  0.908
## Residuals         517 146.15 0.28270               
## 1 observation deleted due to missingness
TukeyHSD(change.anova)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = change_tip ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                         diff        lwr       upr     p adj
## Somewhat important actor-Most important actor   -0.033069677 -0.1876208 0.1214814 0.9461281
## Not an important actor-Most important actor     -0.045175624 -0.2479753 0.1576240 0.9397810
## Don't know-Most important actor                 -0.005923081 -0.1721476 0.1603014 0.9997222
## Not an important actor-Somewhat important actor -0.012105947 -0.2070230 0.1828111 0.9985387
## Don't know-Somewhat important actor              0.027146596 -0.1293639 0.1836571 0.9701783
## Don't know-Not an important actor                0.039252543 -0.1650443 0.2435494 0.9601399

Change in Cho scores

Opinions of importance vary slightly by changes in Cho policy scores. Respondents who indicated that the US was more important tended to work in countries with greater changes in TIP policy.

ggplot(df.importance, aes(x=Q3.19, y=change_policy)) + 
  geom_violin(fill="grey90") + 
  geom_point(stat="summary", fun.y="mean", size=5) + 
  labs(x="Opinion of US", y="Change in TIP policy index") + 
  coord_flip() + theme_clean()

Variance is equal in all groups:

kruskal.test(change_policy ~ importance_factor, data=df.importance)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  change_policy by importance_factor
## Kruskal-Wallis chi-squared = 4.7541, df = 3, p-value = 0.1907

ANOVA shows no differences

cho.change.anova <- aov(change_policy ~ importance_factor, data=df.importance) 
summary(cho.change.anova)  # (⌐○Ϟ○)  ♥  \(•◡•)/
##                    Df Sum Sq Mean Sq F value Pr(>F)
## importance_factor   3     35  11.769   1.766  0.153
## Residuals         517   3445   6.663               
## 1 observation deleted due to missingness
TukeyHSD(cho.change.anova)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = change_policy ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                       diff        lwr       upr     p adj
## Somewhat important actor-Most important actor   -0.3665885 -1.1169034 0.3837264 0.5893507
## Not an important actor-Most important actor     -0.8270207 -1.8115725 0.1575310 0.1344747
## Don't know-Most important actor                 -0.4958620 -1.3028488 0.3111249 0.3888147
## Not an important actor-Somewhat important actor -0.4604322 -1.4067155 0.4858511 0.5926605
## Don't know-Somewhat important actor             -0.1292735 -0.8891009 0.6305540 0.9717760
## Don't know-Not an important actor                0.3311588 -0.6606615 1.3229791 0.8251392

US funding received by the responding organization

Organizations that have been received funding from the US are more likely to consider the US to play an important role in the countries they work in.

funding.table <- df.importance %>%
  xtabs(~ Q3.19 + received.funding, .) %>% print
##                           received.funding
## Q3.19                      FALSE TRUE
##   Most important actor        81   58
##   Somewhat important actor   149   33
##   Not an important actor      65    3
##   Don't know                 131    2

There’s an overall significant difference (though two of the cells are really small here)

(funding.chi <- chisq.test(funding.table))
## 
##  Pearson's Chi-squared test
## 
## data:  funding.table
## X-squared = 84.566, df = 3, p-value < 2.2e-16
# Cramer's V for standardized measure of association
assocstats(funding.table)
##                     X^2 df P(> X^2)
## Likelihood Ratio 91.734  3        0
## Pearson          84.566  3        0
## 
## Phi-Coefficient   : NA 
## Contingency Coeff.: 0.373 
## Cramer's V        : 0.402
# Components of chi-squared
(components <- funding.chi$residuals^2)
##                           received.funding
## Q3.19                             FALSE         TRUE
##   Most important actor      9.275164424 41.158542132
##   Somewhat important actor  0.001495267  0.006635247
##   Not an important actor    1.628262816  7.225416244
##   Don't know                4.647505115 20.623303950
round(1-pchisq(components, funding.chi$parameter), 3)
##                           received.funding
## Q3.19                      FALSE  TRUE
##   Most important actor     0.026 0.000
##   Somewhat important actor 1.000 1.000
##   Not an important actor   0.653 0.065
##   Don't know               0.200 0.000
# Visualize differences
mosaic(funding.table,
       labeling_args=list(set_varnames=c(received.funding="Received US funding", 
                                         Q3.19="Opinion of US"),
                          gp_labels=(gpar(fontsize=8))), 
       gp_varnames=gpar(fontsize=10, fontface=2))

US funding received by the country the organization works in

Opinions of importance are strongly associated with US TIP funding given to a country. Organizations are more likely to think the US is an important actor if they work in countries receiving more anti-TIP funding.

ggplot(df.importance, aes(x=Q3.19, y=log.total.funding)) + 
  geom_violin(fill="grey90") + 
  geom_point(alpha=0.05) + 
  geom_point(stat="summary", fun.y="mean", size=5) + 
  labs(x="Opinion of US", y="Total TIP funding to country (logged)") + 
  scale_y_continuous(labels=trans_format("exp", dollar_format())) +
  coord_flip() + theme_clean()

Variance is not equal in all groups:

kruskal.test(log.total.funding ~ importance_factor, data=df.importance)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  log.total.funding by importance_factor
## Kruskal-Wallis chi-squared = 21.659, df = 3, p-value = 7.68e-05

Ratio is 3ish, which is below 4, so heterogenous variance is okayish:

df.importance %>% group_by(importance_factor) %>%
  summarise(variance = var(log.total.funding, na.rm=TRUE)) %>%
  do(data_frame(ratio = max(.$variance) / min(.$variance)))
## Source: local data frame [1 x 1]
## 
##      ratio
##      (dbl)
## 1 3.006739

ANOVA shows significant differences:

funding.anova <- aov(log.total.funding ~ importance_factor, data=df.importance) 
summary(funding.anova)
##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## importance_factor   3   1234   411.2   11.92 1.48e-07 ***
## Residuals         508  17524    34.5                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingness
(funding.pairs <- TukeyHSD(funding.anova))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log.total.funding ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                      diff       lwr        upr     p adj
## Somewhat important actor-Most important actor   -1.241784 -2.974848  0.4912799 0.2526710
## Not an important actor-Most important actor     -4.368679 -6.625553 -2.1118050 0.0000050
## Don't know-Most important actor                 -3.309266 -5.169210 -1.4493229 0.0000337
## Not an important actor-Somewhat important actor -3.126895 -5.283433 -0.9703573 0.0011831
## Don't know-Somewhat important actor             -2.067482 -3.804308 -0.3306565 0.0121187
## Don't know-Not an important actor                1.059413 -1.200352  3.3191771 0.6217846

See those differences

funding.pairs.plot <- data.frame(funding.pairs$importance_factor) %>%
  mutate(pair = row.names(.),
         pair = factor(pair, levels=pair, ordered=TRUE),
         stars = add.stars(p.adj))

fig.funding.pairs <- ggplot(funding.pairs.plot, 
                            aes(x=pair, y=diff, ymax=upr, ymin=lwr)) + 
  geom_hline(yintercept=0) + 
  geom_text(aes(label=stars), nudge_x=0.25) +
  geom_pointrange() + 
  theme_clean() + coord_flip()
fig.funding.pairs

Democracy (Freedom House political rights + civil liberties)

US importance appears to be associated with the level of democracy in a country. NGOs working in countries with worse democracy (higher numbers of the total freedom scale) are more likely to see the US as the most important anti-TIP actor in that country. Or, rather, on average total freedom is worse in countries where NGOs indicate the US as the most important actor.

ggplot(df.importance, aes(x=Q3.19, y=total.freedom)) + 
  geom_violin(fill="grey90") + 
  geom_point(alpha=0.05) + 
  geom_point(stat="summary", fun.y="mean", size=5) + 
  labs(x="Opinion of US", 
       y="Total freedom (political rights + civil liberties; higher is worse)") + 
  coord_flip() + theme_clean()

Variance is not equal in all groups:

kruskal.test(total.freedom ~ importance_factor, data=df.importance)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  total.freedom by importance_factor
## Kruskal-Wallis chi-squared = 21.262, df = 3, p-value = 9.288e-05

Ratio between min and max variance is low, so we’re okay:

df.importance %>% group_by(importance_factor) %>%
  summarise(variance = var(total.freedom, na.rm=TRUE)) %>%
  do(data_frame(ratio = max(.$variance) / min(.$variance)))
## Source: local data frame [1 x 1]
## 
##      ratio
##      (dbl)
## 1 1.431773

ANOVA shows significant differences:

democracy.anova <- aov(total.freedom ~ importance_factor, data=df.importance) 
summary(democracy.anova)
##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## importance_factor   3    208   69.24   6.486 0.000259 ***
## Residuals         512   5465   10.67                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 6 observations deleted due to missingness
(democracy.pairs <- TukeyHSD(democracy.anova))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = total.freedom ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                          diff       lwr        upr     p adj
## Somewhat important actor-Most important actor   -0.8469545051 -1.804602  0.1106928 0.1041491
## Not an important actor-Most important actor     -1.6001193070 -2.852380 -0.3478582 0.0058153
## Don't know-Most important actor                 -1.5999919833 -2.630782 -0.5692024 0.0004202
## Not an important actor-Somewhat important actor -0.7531648018 -1.950937  0.4446072 0.3677940
## Don't know-Somewhat important actor             -0.7530374782 -1.716898  0.2108229 0.1842105
## Don't know-Not an important actor                0.0001273237 -1.256892  1.2571462 1.0000000

View the differences:

democracy.pairs.plot <- data.frame(democracy.pairs$importance_factor) %>%
  mutate(pair = row.names(.),
         pair = factor(pair, levels=pair, ordered=TRUE),
         stars = add.stars(p.adj))

fig.democracy.pairs <- ggplot(democracy.pairs.plot, 
                            aes(x=pair, y=diff, ymax=upr, ymin=lwr)) + 
  geom_hline(yintercept=0) + 
  geom_text(aes(label=stars), nudge_x=0.25) +
  geom_pointrange() + 
  theme_clean() + coord_flip()
fig.democracy.pairs

TODO: Type of TIP work

Time spent on trafficking

The time NGOs spend on trafficking issues does not appear to be associated with their opinion of US importance.

ggplot(df.importance, aes(x=Q3.19, y=time.spent)) + 
  geom_violin(fill="grey90") + 
  geom_point(alpha=0.05) + 
  geom_point(stat="summary", fun.y="mean", size=5) + 
  labs(x="Opinion of US", y="Time spent on trafficking issues") + 
  coord_flip() + theme_clean()

Variance is not equal in all groups:

kruskal.test(time.spent ~ importance_factor, data=df.importance)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  time.spent by importance_factor
## Kruskal-Wallis chi-squared = 7.5031, df = 3, p-value = 0.05748

Ratio between min and max variance is low, so we’re okay:

df.importance %>% group_by(importance_factor) %>%
  summarise(variance = var(time.spent, na.rm=TRUE)) %>%
  do(data_frame(ratio = max(.$variance) / min(.$variance)))
## Source: local data frame [1 x 1]
## 
##      ratio
##      (dbl)
## 1 1.365997

ANOVA shows some small overall signifcant differences, but when decomposed, that effect is coming only from the tiny “Don’t know-Somewhat important actor” difference.

time.anova <- aov(time.spent ~ importance_factor, data=df.importance) 
summary(time.anova)
##                    Df Sum Sq Mean Sq F value Pr(>F)  
## importance_factor   3   9603    3201     2.9 0.0346 *
## Residuals         491 541945    1104                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 27 observations deleted due to missingness
(time.pairs <- TukeyHSD(time.anova))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = time.spent ~ importance_factor, data = df.importance)
## 
## $importance_factor
##                                                       diff        lwr       upr     p adj
## Somewhat important actor-Most important actor    8.5081361  -1.357648 18.373920 0.1184138
## Not an important actor-Most important actor      7.6875000  -5.294752 20.669752 0.4223730
## Don't know-Most important actor                 -1.0297619 -11.619626  9.560102 0.9944664
## Not an important actor-Somewhat important actor -0.8206361 -13.390747 11.749474 0.9983036
## Don't know-Somewhat important actor             -9.5378980 -19.618277  0.542481 0.0712221
## Don't know-Not an important actor               -8.7172619 -21.863334  4.428810 0.3198290

Interaction with the US

Organizations that have been involved with the US are more likely to consider the US to play an important role in the countries they work in.

involvement.table <- df.importance %>%
  xtabs(~ Q3.19 + us.involvement, .) %>% print
##                           us.involvement
## Q3.19                      FALSE TRUE
##   Most important actor        23  116
##   Somewhat important actor    48  134
##   Not an important actor      38   30
##   Don't know                  73   60

There’s an overall significant difference

(involvement.chi <- chisq.test(involvement.table))
## 
##  Pearson's Chi-squared test
## 
## data:  involvement.table
## X-squared = 63.022, df = 3, p-value = 1.328e-13
# Cramer's V for standardized measure of association
assocstats(involvement.table)
##                     X^2 df   P(> X^2)
## Likelihood Ratio 63.914  3 8.5598e-14
## Pearson          63.022  3 1.3278e-13
## 
## Phi-Coefficient   : NA 
## Contingency Coeff.: 0.328 
## Cramer's V        : 0.347
# Components of chi-squared
(components <- involvement.chi$residuals^2)
##                           us.involvement
## Q3.19                          FALSE      TRUE
##   Most important actor     13.379010  7.161705
##   Somewhat important actor  3.764597  2.015167
##   Not an important actor    8.614436  4.611257
##   Don't know               15.291006  8.185186
1-pchisq(components, involvement.chi$parameter)
##                           us.involvement
## Q3.19                            FALSE        TRUE
##   Most important actor     0.003884710 0.066918431
##   Somewhat important actor 0.288031028 0.569264781
##   Not an important actor   0.034881684 0.202578528
##   Don't know               0.001584118 0.042335569
# Visualize differences
mosaic(involvement.table,
       labeling_args=list(set_varnames=c(us.involvement="US involvement", 
                                         Q3.19="Opinion of US"),
                          gp_labels=(gpar(fontsize=8))), 
       gp_varnames=gpar(fontsize=10, fontface=2))

Does opinion of the US vary by:

  • Tier rating (average) or improvement in Cho score?
  • Whether an NGO has received US funding (or where the COUNTRY has received more TIP grants?)
  • Whether an NGO has interacted with the US
  • Whether a country is rich or poor (or some other quality)
  • Whether an NGO focuses on certain types of work?
  • TODO: In which countries does the US seem to have had more collaboration with NGOs?
  • TODO: Explaining variation in opinion of US positivity? (no because censoring) Opinions are not driven by cooptation - look at chapter 1 for boomerang type stuff - cooptation by donors - so in this case, the NGOs aren’t just being bought out
ggplot(responses.full, aes(x=Q3.25_collapsed, y=total.funding, fill=Q3.25_collapsed)) + 
  geom_violin() + 
  geom_point(alpha=0.3, position=position_jitterdodge()) + 
  labs(x="Opinion of US", y="Total TIP funding to country") + 
  scale_y_continuous(labels=dollar)

ggplot(responses.full, aes(x=Q3.25, y=avg.funding, fill=Q3.25)) + 
  geom_violin() + 
  geom_point(alpha=0.3, position=position_jitterdodge()) + 
  labs(x="Opinion of US", y="Average TIP funding to country") + 
  scale_y_continuous(labels=dollar)

The same is true if looking just at US funding designated for just NGOs and NPOs

ggplot(responses.full, aes(x=Q3.25_collapsed, y=total.funding.ngos, fill=Q3.25_collapsed)) + 
  geom_violin() + 
  geom_point(alpha=0.3, position=position_jitterdodge()) + 
  labs(x="Opinion of US", y="Total NGO-only TIP funding to country") + 
  scale_y_continuous(labels=dollar)

ggplot(responses.full, aes(x=Q3.25_collapsed, y=avg.funding.ngos, fill=Q3.25_collapsed)) + 
  geom_violin() + 
  geom_point(alpha=0.3, position=position_jitterdodge()) + 
  labs(x="Opinion of US", y="Average NGO-only TIP funding to country") + 
  scale_y_continuous(labels=dollar)

plot.data <- responses.full %>% select(Q3.19, total.funding) %>% filter(!is.na(Q3.19))
ggplot(plot.data, aes(x=Q3.19, y=total.funding, fill=Q3.19)) + 
  geom_violin() + 
  geom_point(alpha=0.3, position=position_jitterdodge()) + 
  labs(x="US importance", y="Total TIP funding to country") + 
  scale_y_continuous(labels=dollar) +
  coord_flip()

# Type of work
# TODO: work.country is not the most reliable identifier---there be NAs
# Q2.2_X
asdf <- responses.full %>% 
  select(survey.id, matches("Q2.2_\\d$")) %>%
  gather(type_of_work, value, -survey.id) %>%
  mutate(type_of_work = factor(type_of_work, levels=paste0("Q2.2_", seq(1:4)), 
                               labels=c("Organs", "Sex", "Labor", "Other"), 
                               ordered=TRUE))
asdf %>%
  group_by(type_of_work) %>%
  summarise(bloop = n(),
            derp = sum(value, na.rm=TRUE),
            asdf = derp / n())
## Source: local data frame [4 x 4]
## 
##   type_of_work bloop  derp       asdf
##         (fctr) (int) (int)      (dbl)
## 1       Organs   561    40 0.07130125
## 2          Sex   561   483 0.86096257
## 3        Labor   561   345 0.61497326
## 4        Other   561   140 0.24955437
# Should be 30, 408, 294, 116 with 479 total

# Q2.3_
# Q2.4_X


# Opinion of the US vs. importance
# Can't do this because 3.25 is censored by 3.19
responses.full %>% 
  xtabs(~ Q3.25 + Q3.19, .)
##             Q3.19
## Q3.25        Most important actor Somewhat important actor Not an important actor Don't know
##   Don't know                    8                       28                      0          0
##   Mixed                        21                       43                      0          0
##   Negative                      2                        0                      0          0
##   Positive                    107                      107                      0          0
table(responses.full$Q3.25)
## 
## Don't know      Mixed   Negative   Positive 
##         36         64          2        214
table(responses.full$Q3.19)
## 
##     Most important actor Somewhat important actor   Not an important actor               Don't know 
##                      139                      182                       68                      133
sum(table(responses.full$Q3.25))
## [1] 316
sum(table(responses.full$Q3.19))
## [1] 522
responses.full$Q3.19 %>%
  table %>% print %>% prop.table
## .
##     Most important actor Somewhat important actor   Not an important actor               Don't know 
##                      139                      182                       68                      133
## .
##     Most important actor Somewhat important actor   Not an important actor               Don't know 
##                0.2662835                0.3486590                0.1302682                0.2547893
# Find country averages of government improvement, etc. - then show that X number of countries show improvement, etc. 
# Report by organization and by country - how many countries has the US had a positive influence + how many NGOs say the US has had a positive influence


full <- left_join(country.indexes, tip.change,
                  by=c("work.country" = "countryname")) %>%
  filter(num.responses >= 10) %>%
  mutate(country_label = ifelse(num.responses >= 10, work.country, ""))


ggplot(full, aes(x=improvement_score, y=change_policy, label=work.country)) + 
  geom_point() + geom_text(vjust=1.5) +
  geom_hline(yintercept=0) + 
  scale_x_continuous(limits=c(0, 1)) + 
  scale_y_continuous(limits=c(-2, 6))

ggplot(country.indexes, aes(x=work.country, y=improvement_score)) + 
  geom_bar(stat="identity") + 
  coord_flip()

# Compare improvement scores with actual changes in TIP score to get a sense of if NGO experiences reflect changes in rankings

ggplot(country.indexes, aes(x=work.country, y=positivity_score)) + 
  geom_bar(stat="identity") + 
  coord_flip()

responses.countries %>% 
  xtabs(~ Q3.25 + Q3.19, .)
##             Q3.19
## Q3.25        Most important actor Somewhat important actor Not an important actor Don't know
##   Negative                      2                        0                      0          0
##   Positive                    107                      107                      0          0
##   Mixed                        21                       43                      0          0
##   Don't know                    8                       28                      0          0
# Importance opinions
importance.opinions <- responses.all %>%
  filter(Q3.19 == "Not an important actor") %>%
  select(survey.id, clean.id, Q3.19, contains("TEXT"), Q4.1)

responses.all$Q3.19 %>%
  table %>% print %>% prop.table
## .
##     Most important actor Somewhat important actor   Not an important actor               Don't know 
##                      139                      182                       68                      133
## .
##     Most important actor Somewhat important actor   Not an important actor               Don't know 
##                0.2662835                0.3486590                0.1302682                0.2547893
responses.countries %>% 
  xtabs(~ Q3.25 + Q3.26, .)
##             Q3.26
## Q3.25        Improved Remained constant Slowed down Don't know
##   Negative          2                 0           0          0
##   Positive        150                39          22          3
##   Mixed            33                16          13          2
##   Don't know       19                 8           6          3
ggplot(responses.orgs, aes(x = Q1.5.factor)) + geom_bar() + 
  labs(x = Hmisc::label(responses.orgs$Q1.5))

# Importance of US
asdf <- responses.all %>% 
  select(clean.id, Q1.2, Q3.8, Q3.6, Q3.7)

inconsistent.no <- c(1020, 1152, 1226, 1267, 1323, 1405, 1515)
inconsistent.dont.know <- c(1051, 1512)

qwer <- asdf %>%
  mutate(us.active = ifelse(clean.id %in% c(inconsistent.no, inconsistent.dont.know),
                            "Yes", as.character(Q3.8)))

qwer$us.active %>% table %>% print %>% prop.table
## .
## Don't know         No        Yes 
##        148         32        353
## .
## Don't know         No        Yes 
## 0.27767355 0.06003752 0.66228893
sdfg <- qwer %>% group_by(clean.id) %>% 
  summarize(said.no = ifelse(any(us.active == "No", na.rm=TRUE), TRUE, FALSE))
sdfg$said.no %>% table %>% print %>% prop.table
## .
## FALSE  TRUE 
##   491    31
## .
##      FALSE       TRUE 
## 0.94061303 0.05938697
# US importance and positivity



# Importance of report 
responses.orgs$Q2.5 %>% table %>% print %>% prop.table
## .
##  No Yes 
##  67 452
## .
##        No       Yes 
## 0.1290944 0.8709056
responses.countries$Q3.23 %>% table %>% print %>% prop.table
## .
##  No Yes 
## 265 205
## .
##        No       Yes 
## 0.5638298 0.4361702
heard.of.tip <- responses.countries %>% 
  left_join(responses.orgs, by="survey.id") %>%
  filter(Q2.5 == "Yes") %>%
  group_by(survey.id) %>%
  mutate(know.score = ifelse(Q3.22 == "Don't know", FALSE, TRUE)) %>%
  select(know.score) %>% unique

heard.of.tip$know.score %>% table %>% print %>% prop.table
## .
## FALSE  TRUE 
##    98   311
## .
##     FALSE      TRUE 
## 0.2396088 0.7603912
# Opinions of report
opinions <- responses.all %>% 
  select(clean.id, Q1.2, home.country, work.country, Q3.21_1, Q3.21_4_TEXT, Q3.24.Text)

not.used.tip.ids <- c(1094, 1099, 1106, 1114, 1157, 1221, 1244, 1269, 
                      1314, 1330, 1354, 1357, 1393, 1425)
not.used.tip <- responses.all %>%
  mutate(no.response = ifelse(is.na(Q3.21_1) & is.na(Q3.21_2) & 
                                is.na(Q3.21_3) & is.na(Q3.21_4), TRUE, FALSE),
         explicit.no = ifelse(clean.id %in% not.used.tip.ids, TRUE, FALSE)) %>%
  select(clean.id, Q1.2, Q3.21_1, Q3.21_2, Q3.21_3, Q3.21_4, no.response, explicit.no) %>%
  group_by(clean.id) %>%
  summarize(no.response = ifelse(sum(no.response) > 0, TRUE, FALSE),
            explicit.no = ifelse(sum(explicit.no) > 0, TRUE, FALSE))

not.used.tip$explicit.no %>% table
## .
## FALSE  TRUE 
##   508    14
# Does opinion of the US vary by:
# * Tier rating (average) or improvement in Cho score?
# * Whether an NGO has received US funding (or where the COUNTRY has received more TIP grants?)
# * Whether an NGO has interacted with the US
# * Whether a country is rich or poor (or some other quality)
# * Whether an NGO focuses on certain types of work?
# * In which countries does the US seem to have had more collaboration with NGOs?